High-Fidelity Image Synthesis from Pulmonary Nodule Lesion Maps using Semantic Diffusion Model

Lung cancer has been one of the leading causes of cancer-related deaths worldwide for years. With the emergence of deep learning, computer-assisted diagnosis (CAD) models based on learning algorithms can accelerate the nodule screening process, providing valuable assistance to radiologists in their daily clinical workflows. However, developing such robust and accurate models often requires large-scale and diverse medical datasets with high-quality annotations. Generating synthetic data provides a pathway for augmenting datasets at a larger scale. Therefore, in this paper, we explore the use of Semantic Diffusion Mod- els (SDM) to generate high-fidelity pulmonary CT images from segmentation maps. We utilize annotation information from the LUNA16 dataset to create paired CT images and masks, and assess the quality of the generated images using the Frechet Inception Distance (FID), as well as on two common clinical downstream tasks: nodule detection and nodule localization. Achieving improvements of 3.96% for detection accuracy and 8.50% for AP50 in nodule localization task, respectively, demonstrates the feasibility of the approach.Comment: 4 pages, 1 figure, submitted to MIDL 202

The difference between the domination number and the minus domination number of a cubic graph

AbstractThe closed neighborhood of a vertex subset S of a graph G = (V, E), denoted as N[S], is defined as the union of S and the set of all the vertices adjacent to some vertex of S. A dominating set of a graph G = (V, E) is defined as a set S of vertices such that N[S] = V. The domination number of a graph G, denoted as γ(G), is the minimum possible size of a dominating set of G. A minus dominating function on a graph G = (V, E) is a function g : V → {−1, 0, 1} such that g(N[v]) ≥ 1 for all vertices. The weight of a minus dominating function g is defined as g(V) =ΣvϵVg(v). The minus domination number of a graph G, denoted as γ−(G), is the minimum possible weight of a minus dominating function on G. It is well known that γ−(G) ≤ γ(G). This paper is focused on the difference between γ(G) and γ−(G) for cubic graphs. We first present a graph-theoretic description of γ−(G). Based on this, we give a necessary and sufficient condition for γ(G) −γ−(G) ≥ k. Further, we present an infinite family of cubic graphs of order 18k + 16 and with γ(G) −γ−(G) ≥

Stress Relaxation in Entangled Polymer Melts

We present an extensive set of simulation results for the stress relaxation in equilibrium and step-strained bead-spring polymer melts. The data allow us to explore the chain dynamics and the shear relaxation modulus, $G(t)$, into the plateau regime for chains with $Z=40$ entanglements and into the terminal relaxation regime for $Z=10$. Using the known (Rouse) mobility of unentangled chains and the melt entanglement length determined via the primitive path analysis of the microscopic topological state of our systems, we have performed parameter -free tests of several different tube models. We find excellent agreement for the Likhtman-McLeish theory using the double reptation approximation for constraint release, if we remove the contribution of high-frequency modes to contour length fluctuations of the primitive chain.Comment: 5 pages, 3 figure

Survival of esophageal cancer in China: A pooled analysis on hospital-based studies from 2000 to 2018

Background: Esophageal cancer (EC) causes more than 400 thousand deaths per year, and half of them occur in China. There are discrepancies regarding the survival of EC patients between population-based surveillance studies and hospital-based studies. Objectives: We aimed to synthesize the survival data from hospital-based EC studies in the Chinese population from 2000 to 2018 and to compare the survival rates between EC patients with different clinical classifications. Methods: The protocol of this systematic review was registered in PROSPERO (CRD-42019121559). We searched Embase, PubMed, CNKI, and Wanfang databases for studies published between January 1, 2000 and December 31, 2018. We calculated the pooled survival rates and 95% confidence intervals (CIs) by Stata software (V14.0). Results: Our literature search identified 933 studies, of which 331 studies with 79,777 EC patients met the inclusion criteria and were included in meta-analyses. The pooled survival rates were 74.1% (95% CI: 72.6–75.7%) for 1-year survival, 49.0% (95% CI: 44.2–53.8%) for 2-years survival, 46.0% (95% CI: 42.6–49.5%) for 3-years survival, and 40.1% (95% CI: 33.7–46.4%) for 5-years survival. An increased tendency toward EC survival was verified from 2000 to 2018. In addition, discrepancies were observed between EC patients with different clinical classifications (e.g., stages, histologic types, and cancer sites). Conclusions: Our findings showed a higher survival rate in hospital-based studies than population-based surveillance studies. Although this hospital-based study is subject to potential representability and publication bias, it offers insight into the prognosis of patients with EC in China